基于光纤视频测井技术的孔眼自动识别

Automatic Aperture Identification Based on Fiber Video Well Logging Technique

针对目前光纤测井视频资料处理过程中存在的问题,提出一种孔眼信息自动识别处理技术,采用选择性高斯拉普拉斯边缘检测及双向矩形框环形扫描技术,实现孔眼的自动识别,通过特定的后期纠正算法,实现数据库中孔眼信息的自动纠正,并用像素差值法进行孔眼的出油判断。仿真实验结果表明,该技术处理速度快、准确率高,具有一定应用价值。

Aiming at the problems in process of fiber video well logging processing, a novel technique of automatic apertures identification and processing is proposed, which uses selective Gauss Laplace edge detection and annular two-way rectangular box scanning technique to implement automatic aperture identification. It corrects apertures information in the database automatically with special algorithm, and estimates whether the aperture produces oil by calculating margin of certain pixel counts. Simulation experimental results demonstrate this technique can get a fast processing speed and high accuracy rate, which has the value of application.